A Constraint Programming approach for collective smart building scheduling improved by blockchain structure

[1]  M. Maaroufi,et al.  An Intelligent Demand Side Management Algorithm for Smart buildings , 2021, 2021 12th International Renewable Engineering Conference (IREC).

[2]  Lisa Bosman,et al.  Peer-to-peer energy trading: A review of the literature , 2020, Applied Energy.

[3]  Saad Mekhilef,et al.  Progress on the demand side management in smart grid and optimization approaches , 2020, International Journal of Energy Research.

[4]  Mohammed Ouassaid,et al.  Collective demand side management in smart grids , 2020, 2020 5th International Conference on Renewable Energies for Developing Countries (REDEC).

[5]  Hafiz Majid Hussain,et al.  A Heuristic-based Home Energy Management System for Demand Response , 2020, 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS).

[6]  Jaeyoung Park,et al.  Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System , 2020, Energies.

[7]  Majid Jamil,et al.  Hourly load shifting approach for demand side management in smart grid using grasshopper optimisation algorithm , 2020 .

[8]  Bilal Toklu,et al.  Problem Specific Variable Selection Rules for Constraint Programming: A Type II Mixed Model Assembly Line Balancing Problem Case , 2020, Appl. Artif. Intell..

[9]  Haibo He,et al.  Automated Demand Response Framework in ELNs: Decentralized Scheduling and Smart Contract , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Hoay Beng Gooi,et al.  Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model , 2019, IEEE Transactions on Industrial Electronics.

[11]  Nadeem Javaid,et al.  Hybrid meta-heuristic optimization based home energy management system in smart grid , 2019, Journal of Ambient Intelligence and Humanized Computing.

[12]  Zhou Su,et al.  BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network , 2019, IEEE Transactions on Industrial Informatics.

[13]  Amin Kargarian,et al.  Chance-Constrained Microgrid Energy Management with Flexibility Constraints Provided by Battery Storage , 2019, 2019 IEEE Texas Power and Energy Conference (TPEC).

[14]  D. Jenkins,et al.  Blockchain technology in the energy sector: A systematic review of challenges and opportunities , 2019, Renewable and Sustainable Energy Reviews.

[15]  Ameena Saad Al-Sumaiti,et al.  Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources , 2018, Energies.

[16]  Jie Li,et al.  A Comparison of Constraint Programming and Mixed-Integer Programming for Automated Test-Form Generation , 2018, Journal of Educational Measurement.

[17]  Nadeem Javaid,et al.  An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes , 2018, Energies.

[18]  Xiaonan Wang,et al.  Energy Demand Side Management within micro-grid networks enhanced by blockchain , 2018, Applied Energy.

[19]  M. Pahle,et al.  Time-varying electricity pricing and consumer heterogeneity: Welfare and distributional effects with variable renewable supply , 2018, Energy Economics.

[20]  Meng Cheng,et al.  Peer-to-Peer energy trading in a Microgrid , 2018, Applied Energy.

[21]  Caroline Plaza,et al.  Distributed Solar Self-Consumption and Blockchain Solar Energy Exchanges on the Public Grid Within an Energy Community , 2018, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[22]  Hussain Shareef,et al.  Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers , 2018, IEEE Access.

[23]  A. Schutt,et al.  Mixed-integer linear programming and constraint programming formulations for solving resource availability cost problems , 2018, European Journal of Operational Research.

[24]  Haluk Damgacioglu,et al.  A δ -constraint multi-objective optimization framework for operation planning of smart grids , 2018 .

[25]  Amin Safari,et al.  Real-time based approach for intelligent building energy management using dynamic price policies , 2018 .

[26]  Ming Jin,et al.  Microgrid to enable optimal distributed energy retail and end-user demand response , 2018 .

[27]  Christof Weinhardt,et al.  Designing microgrid energy markets , 2018 .

[28]  Ying Zhong,et al.  M2M Blockchain: The Case of Demand Side Management of Smart Grid , 2017, 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS).

[29]  Rosario Morello,et al.  A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future , 2017, IEEE Sensors Journal.

[30]  Ahmet zk,et al.  A novel metaheuristic for multi-objective optimization problems , 2017 .

[31]  M. P. Selvan,et al.  Dynamic demand response in smart buildings using an intelligent residential load management system , 2017 .

[32]  Abdellatif Miraoui,et al.  Coordinated neighborhood energy sharing using game theory and multi-agent systems , 2017, 2017 IEEE Manchester PowerTech.

[33]  Jianzhong Wu,et al.  Review of Existing Peer-to-Peer Energy Trading Projects , 2017 .

[34]  Luluwah Al-Fagih,et al.  Recent Advances in Local Energy Trading in the Smart Grid Based on Game-Theoretic Approaches , 2017, IEEE Transactions on Smart Grid.

[35]  Nikhil Swamy,et al.  Formal Verification of Smart Contracts: Short Paper , 2016, PLAS@CCS.

[36]  K. Sathish Kumar,et al.  A survey on residential Demand Side Management architecture, approaches, optimization models and methods , 2016 .

[37]  Michael Devetsikiotis,et al.  Blockchains and Smart Contracts for the Internet of Things , 2016, IEEE Access.

[38]  Vikram Kumar Kamboj,et al.  Solution of non-convex economic load dispatch problem for small-scale power systems using ant lion optimizer , 2016, Neural Computing and Applications.

[39]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[40]  Lingfeng Wang,et al.  Smart charging and appliance scheduling approaches to demand side management , 2014 .

[41]  Massoud Tabesh,et al.  Ant-colony optimization of pumping schedule to minimize the energy cost using variable-speed pumps in water distribution networks , 2014 .

[42]  Antonio J. Nebro,et al.  A survey of multi-objective metaheuristics applied to structural optimization , 2014 .

[43]  Saifur Rahman,et al.  Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities , 2014, IEEE Transactions on Smart Grid.

[44]  Walid Saad,et al.  Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications , 2012, IEEE Signal Processing Magazine.

[45]  Zhu Han,et al.  Demand side management to reduce Peak-to-Average Ratio using game theory in smart grid , 2012, 2012 Proceedings IEEE INFOCOM Workshops.

[46]  V. Kamuni,et al.  Secured Energy Trading Using Byzantine-Based Blockchain Consensus , 2020, IEEE Access.

[47]  Cristina Ribeiro,et al.  Optimality in nesting problems: New constraint programming models and a new global constraint for non-overlap , 2019, Operations Research Perspectives.

[48]  Mary Lacity,et al.  Addressing Key Challenges to Making Enterprise Blockchain Applications a Reality , 2018, MIS Q. Executive.

[49]  R. Schubert Purchasing Energy-Efficient Appliances – To Incentivise or to Regulate? , 2017 .

[50]  Bruce A. Conway,et al.  Evolutionary and heuristic methods applied to problems in optimal control , 2016 .

[51]  P. V. Beek,et al.  Handbook of Knowledge Representation Edited Constraint Programming , 2022 .